In magnetic storage systems for computers, a digital data sequence serves to modulate current in a read/write head coil in order to write a corresponding sequence of magnetic flux transitions onto the surface of a magnetic medium in concentric, radially spaced tracks at a predetermined baud rate. To read this recorded data, the read/write head again passes over the magnetic medium and transduces the magnetic transitions into pulses of alternating polarity in a continuous time analog read signal. These pulses are decoded by read channel circuitry to reproduce the digital data sequence.
Decoding the pulses into the digital data sequence can be performed by a peak detector in a conventional analog read channel. In such conventional peak detectors, analog circuitry, responsive to threshold crossing or derivative information, detects peaks in the continuous time analog read signal generated by the read head. The continuous time analog read signal is segmented into bit cell periods and interpreted during these bit cell periods. The presence of a peak during the bit cell period is detected as a "1" bit, whereas the absence of a peak is detected as a "0" bit.
As magnetic flux transitions are packed closer together on the magnetic medium in an effort to increase data density, adjacent pulses begin to overlap with one another, resulting in a type of distortion, known as intersymbol interference (ISI), in the read signal. Intersymbol interference can cause a peak to shift out of its bit cell, or to decrease in magnitude. This can lead to detection errors.
The most common detection errors occur when the bit cells are not correctly aligned with the pulses in the continuous time analog read signal. Timing recovery, then, adjusts the bit cell periods so that peaks of the continuous time analog read signal occur in the center of the bit cells on average in order to minimize detection errors. Since timing information is derived only when peaks are detected, the input digital data sequence is normally run length limited (RLL) to place an upper limit on the number of consecutive "0" bits.
The ISI effect can be reduced by decreasing the data density or by employing a coding scheme that places a lower limit on the number of "0" bits that occur between "1" bits. Thus, a compromise must be reached between the requirement to reduce ISI (which calls for a large number of consecutive "0" bits), and the need for timing recovery (which calls for a small number of consecutive "0" bits). A (d,k) run-length limited (RLL) code constrains the minimum number of "0" bits between "1" bits to d, and the maximum number of consecutive "0" bits to k.
Discrete time sequence detectors in sampled amplitude read channels can compensate for limited amounts of intersymbol interference and are less susceptible to channel noise than analog peak detectors. As a result, discrete time sequence detectors increase the capacity and reliability of the magnetic storage system, and they are therefore preferred over simple analog peak detectors.
There are several well known discrete time sequence detection methods including discrete time pulse detection (DPD), partial response (PR) with Viterbi detection, maximum likelihood sequence detection (MLSD), decision-feedback equalization (DFE), enhanced decision-feedback equalization (EDFE), and fixed-delay tree-search with decision-feedback (FDTS/DF).
Sampled amplitude detection, such as partial response with Viterbi detection, allows for increased data density by compensating for intersymbol interference and the effect of channel noise. Unlike conventional peak detection systems that detect the presence or absence of a peak, sampled amplitude recording detects digital data by interpreting, at discrete time instances, the actual value of the pulse data.
To this end, the read channel comprises a sampling device for sampling the analog read signal, a timing recovery circuit for synchronizing the samples to the baud rate (code bit rate), a low-pass analog filter to process the read signal before it is sampled so as to reduce the effects of aliasing, a digital equalizing filter to equalize the sample values according to a desired partial response after the signal has been sampled, and a discrete time sequence detector, such as a Viterbi detector, to interpret the equalized sample values in context to determine a most likely sequence for the digital data sequence (i.e., using maximum likelihood sequence detection). In this manner, a sampled amplitude read channel can take into account the effect of ISI and channel noise during the detection process, thereby decreasing the probability of a detection error. This increases the effective signal to noise ratio and, for a given (d,k) constraint, allows for significantly higher data density as compared to conventional analog peak detection read channels.
The application of sampled amplitude techniques to digital communication channels is well documented. See, for example:
Y. Kabal and S. Pasupathy, "Partial Response Signaling," IEEE Trans. Commun. Tech., Vol. COM-23, pp. 921-934, Sep. 1975; PA0 E. A. Lee and D. G. Messerschmitt, "Digital Communication," Kluwer Academic Publishers, Boston, 1990; and PA0 G. D. Forney, Jr., "The Viterbi Algorithm," Proc. IEEE, Vol. 61, pp. 268-278, Mar. 1973. PA0 Cideciyan et al, "A PRML System for Digital Magnetic Recording," IEEE Journal on Selected Areas in Communications, Vol. 10, No. 1, pp. 38-56, Jan. 1992; PA0 Wood et al, "Viterbi Detection of Class IV Partial Response on a Magnetic Recording Channel," IEEE Trans. Commun., Vol. COM-34, No. 5, pp. 454-461, May 1986; PA0 Coker et al, "Implementation of PRML in a Rigid Disk Drive," IEEE Trans. on Magnetics, Vol. 27, No. 6, Nov. 1991; PA0 Carley et al, "Adaptive Continuous-Time Equalization Followed by FDTS/DF Sequence Detection," Digest of the Magnetic Recording Conference, Aug. 15-17, 1994, p. C3; PA0 Moon et al, "Constrained Complexity Equalizer Design for Fixed Delay Tree Search with Decision Feedback," IEEE Trans. on Magnetics, Vol. 30, No. 5, Sep. 1994; PA0 Abbott et al, "Timing Recovery for Adaptive Decision Feedback Equalization of the Magnetic Storage Channel," Globecom '90, IEEE Global Telecommunication Conference, 1990, San Diego, Calif., Nov. 1990, pp. 1794-1799; PA0 Abbott et al, "Performance of Digital Magnetic Recording with Equalization and Offtrack Interference," IEEE Trans. on Magnetics, Vol. 27, No. 1, Jan. 1991; PA0 Cioffi et al, "Adaptive Equalization in Magnetic-Disk Storage Channels," IEEE Communication Magazine, Feb. 1990; and PA0 R. Wood, "Enhanced Decision Feedback Equalization," Intermag '90.
Applying sampled amplitude techniques to magnetic storage systems is also well documented. See, for example:
Several forms of distortion in addition to intersymbol interference are present in the analog read signal. One such form of distortion that is particularly troublesome at high data densities is caused by partial erasure of magnetically-stored information. This occurs because each bit of information may, for example, be stored as an individual region in which most magnetic domains are oriented in the same direction. However, the transition from one orientation to another is not abrupt but takes place over a finite distance, and so, at high transition densities, adjacent regions of magnetization interact to reduce the strength of magnetization of individual regions. For example, if the magnetization in a particular region has a predominantly north-south orientation and both of its neighboring regions have similar orientations, then the strength of its magnetization will be substantially unaltered by its neighbors. However, if it has one neighbor with an opposite (i.e., south-north) orientation, the border with this neighbor will be ill-defined and the net magnetization of the region will be reduced. This reduction in magnetization is exacerbated if both of the region's neighbors have magnetic orientations opposite to its own.
The reduction of the strength of magnetization of regions with differently oriented neighbors, referred to as partial erasure, reduces the amplitude of corresponding pulses in the read signal. Thus decoders that do not account for partial erasure are not optimal.
One prior art solution to this problem is described in "Modified Maximum Likelihood Sequence Estimation in a Simple Partial Erasure Model," by I. Lee, T. Yamauchi, and J. M. Cioffi, IEEE International Conference on Communications, May 1-5, 1994, New Orleans. This paper describes how a sequence detector can be matched to a read signal that includes partial erasure. However, it also explains that conventional channel models for describing partial erasure may result in detectors with prohibitive complexity. The paper presents a simplified partial erasure model in which the amplitude of a pulse is reduced by a factor .gamma.&lt;1 if the magnetic transition that caused it has one neighboring transition, and is reduced by a factor .gamma..sup.2 if the transition that caused it has two neighboring transitions. The authors derive a state machine model for a reduced complexity sequence detector matched to partial erasure in a PR4 d=0 read channel. The state machine has been reduced to eleven states. However, such an eleven state machine is still not the optimal implementation.
It is a general aspect of the present invention to implement a PR4 d=0 read channel comprising a sequence detector which operates according to a state machine matched to partial erasure and having less than eleven states.
It is a further aspect of the invention to implement a read channel comprising a sequence detector which operates according to a state machine matched to partial erasure and a coding scheme which increases a minimum distance error event of the sequence detector.
It is a further aspect of the present invention to provide an EPR4 d=1 read channel signal comprising a sequence detector that matched to partial erasure.
It is a further aspect of the present invention to provide an EEPR4 d=1 read channel signal comprising a sequence detector that matched to partial erasure.